LSTM [1]} is first presented to solve complex artificial long-time-lag tasks. As the GWO algorithm [2]} mimics the leadership hierarchy and hunting mechanism of grey wolves in nature, it could be used to optimize the LSTM model. Our model has a significant optimization effect compared to the state-of-the-art LSTM neural networks and BP neural networks. Thus, we use the GWO-LSTM method to complete the post-processing of CEEMDAN-SE. The general process of the GWO-LSTM framework part is shown in Fig. REF .
